Implementation of an automated workflow for image-based seafloor classification with examples from manganese-nodule covered seabed areas in the Central Pacific Ocean

Mapping and monitoring of seafloor habitats are key tasks for fully understanding ocean ecosystems and resilience, which contributes towards sustainable use of ocean resources. Habitat mapping relies on seafloor classification typically based on acoustic methods, and ground truthing through direct sampling and optical imaging. With the increasing capabilities to record high-resolution underwater images, manual approaches for analyzing these images to create seafloor classifications are no longer feasible. Automated workflows have been proposed as a solution, in which algorithms assign pre-defined seafloor categories to each image. However, in order to provide consistent and repeatable analysis, these automated workflows need to address e.g., underwater illumination artefacts, variances in resolution and class-imbalances, which could bias the classification. Here, we present a generic implementation of an Automated and Integrated Seafloor Classification Workflow (AI-SCW). The workflow aims to classify the seafloor into habitat categories based on automated analysis of optical underwater images with only minimal amount of human annotations. AI-SCW incorporates laser point detection for scale determination and color normalization. It further includes semi-automatic generation of the training data set for fitting the seafloor classifier. As a case study, we applied the workflow to an example seafloor image dataset from the Belgian and German contract areas for Manganese-nodule exploration in the Pacific Ocean. Based on this, we provide seafloor classifications along the camera deployment tracks, and discuss results in the context of seafloor multibeam bathymetry. Our results show that the seafloor in the Belgian area predominantly comprises densely distributed nodules, which are intermingled with qualitatively larger-sized nodules at local elevations and within depressions. On the other hand, the German area primarily comprises nodules that only partly cover the seabed, and these occur alongside turned-over sediment (artificial seafloor) that were caused by the settling plume following a dredging experiment conducted in the area.

Second, adaptive histogram equalization is applied to maximize contrast in each image. Therefore, the normalized histogram H of I (IL) is computed (|H| = 256, ∑ = 1).

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Each pixel of color pc in I (IL) is then mapped to a color qc to create an image I (HE) , where qc is determined by: Finally, color normalization is applied to each I (HE) to equalize all N=40,678 images together to a reference illumination. The one image ̅ ( )  The color normalized image I (IN) is then formed from I (HE) . This is done by mapping each value r from the source distribution S, to the corresponding value z that has the same probability in the reference distribution G: Figure S1: Distribution of image scales as a function of acquisition time during the OFOS dive at station 126. The reference image to be used for color normalization is chosen to have the maximum resolution.

Relationship between image-based seafloor classification and acoustic-derived properties
This section provides a very brief summary of the analysis of hydroacoustic data recorded during the multibeam mapping performed during cruise SO268 1 . We follow this with a brief discussion, in which we compare instances of our Seafloor B, C and D against seafloor morphology and multibeam backscatter values obtained from the hydroacoustic analysis. Seafloor A was omitted from this comparison since it was artificial seafloor, which was formed as a result of the settling sediment plume following the dredging experiment done in the German contract area. The aim of this comparison was to further investigate the spatial distribution of our seafloor classes, and also to check if these classes are meaningful e.g., for potentially large-scale habitat classification purposes.
We clarify that this section does not provide an in-depth description of the acoustic data processing methods e.g., for interpreting geological and geomorphological characteristics for purposes of quantifying and assessing the concentration of Mn-nodules on large areas of the seabed. For a comprehensive description and in-depth discussion of these aspects, we point interested readers to recent studies such as 2 , 3 , 4 , 5 , 6 and 7 . In addition, we note that compared to the optical images recorded by the OFOS, the resolution of our 12kHz ship-based hydroacoustic dataset that was recorded from an average of 4,280 m water depth may not be sufficient for showing fine scale variation in seafloor classes in certain applications.
Both the raw and processed bathymetry and multibeam backscatter datasets were acquired during the same cruise SO268 that also acquired the underwater optical images used in this study, and these acoustic datasets published in PANGAEA 8 , 9 . These datasets were analyzed to generate seafloor morphological properties, which were compared against the seafloor classification results obtained from our image-based workflow. This was aimed at checking whether our proposed image-based workflow produces semantically meaningful seafloor substrate classes that can be potentially used for habitat classification, and also to further investigate the distribution of Mn-nodule on the seafloor.
The generated seafloor morphological properties included absolute depth, backscatter, slope, bathymetric positioning index (BPI), and ruggedness -expressed as Vector Ruggedness Measure 10 . Slope, ruggedness, and BPI raster grids were calculated using the Benthic Terrain Modeler 11 in ArcMap 10.6, with a spatial resolution of 50 x 50m 9 . A 3 x 3 neighbor was used for the slope and ruggedness calculation, whereas the BPI was calculated with an inner radius of 50m and an outer radius of 300 meters scale. The BPIs were standardized to enable comparison between the different scales and areas. In both contract areas, the same multibeam (EM122) and acquisition settings were used 1 . Post-processing of the backscatter data utilized the Fledermaus Geocoder Toolbox (FMGT) by QPS, applying the same settings for both areas. Thus, a comparison between the two contract areas is feasible. For a more in depth description of the methods, as well as comprehensive discussion of geological reasons for the Mn-nodule distributions please see 2 , 3 , 4 .
The five investigated seafloor morphological properties for both the German and Belgian contract areas are shown in supplementary Fig. S2. The German contract area is shallower than the Belgian area; differences in backscatter intensity are also visible in the two contract areas. In the German area, a difference in backscatter intensity can be seen between the Seafloor classes B, C and D. Compared to seafloor regions with sparsely distributed Mn-nodules in Seafloor B, the backscatter strength is higher in regions with dense and large Mn-nodules (Seafloor C and D). This is because the acoustic signal penetrates into the sediment, and is less scattered from the rough microrelief of the Mn-nodule seafloor in patchy Mn-nodule areas (Seafloor B) compared to Seafloor C and D. This is consistent with previous findings which found an association between low backscatter strength for a seabed that is dominated by sediment, whereas higher backscatter strength was associated with medium-to-large sized Mn-nodules including outcropping rocks 12 , 4 , 5 . Overall, the large Mn-nodule areas (Seafloor D) returned the highest backscatter, even though in the Belgian contract area the relative difference in the backscatter strength between densely distributed and large Mn-nodules was only marginal. This could be because the two contract areas contributed disproportionately to each seafloor class. The Belgian contract area contributed significantly to both Seafloor C (96%) and Seafloor D (64%), while the German area contributed significantly to Seafloor D (35%) but insignificantly to Seafloor C (3%). This could explain why the relative difference in backscatter strength between Seafloor C and D in the German area was more pronounced compared to those in the Belgian area. The disproportionality of the Mn-nodules distribution was also observed in previous studies by 13 and 14 .
Comparing the two contract areas, the median backscatter strength in the Belgian area was consistently lower than in the German area, although large and dense nodules should give a higher backscatter response than the smaller and patchy distribute occurrences. We note that for 12kHz MBES systems, the recorded backscatter signal reflects both the top of the seafloor (the top 20cm; the seafloor backscatter) but also reaches several meters into the sediment column (volume backscatter; Mitchell, 1993) 16 , 17 . Especially in the German contract area, a high percentage of larger Mn-nodules (30%) is buried in the first 20 cm of the sediment column. This percentage can vary significantly (20-70%) thereby contributing to the observed backscatter intensity 18 . Opposite, the Belgian contract area has a smaller percentage of buried Mn-nodules 19 . In addition, the Belgian area lies 400m deeper than the German area, a fact that could have influenced the backscatter correction of the received signal intensity from different water depths during the acquisition or/and backscatter processing in FMGT 20 21 . Moreover, the relative inconsistency in backscatter intensity between the two areas could be caused by the difference in spatial footprint of the images used in the classification (1.6 m 2 ), and the spatial footprint of the backscatter dataset (the beam footprint increases with depth). In a previous study, 12 point out that some level of inconsistency between in-situ observations by the OFOS and backscatter should be expected if the spatial footprints are so much different between visual and hydroacoustic observations.
Analysis of the fine BPI revealed that the large Mn-nodules were located in relatively flat regions characterized with a median BPI value of zero, while the densely distributed Mn-nodules occupied local elevations (e.g., ridges) with positive median BPI value (+1). The few sparsely distributed Mn-nodules were located in valleys with a negative median BPI value (-2). In the Belgian area, positive BPI values clearly distinguishes Seafloor C from the other classes B and D, which shows that Seafloor C occurs mainly in the local elevations of the seafloor.
Correlations between the BPI and Mn-nodule distribution have also been detected in previous studies by 7 , 4 and 3 . However, we emphasize that these studies were based on AUV-derived bathymetry data, which has at higher resolution than our ship-based bathymetric data.